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Record W2290123976 · doi:10.1002/cjce.22455

Two‐phase flow boiling in 19 mm tube: Experiments and CFD modelling

2016· article· en· W2290123976 on OpenAlexvenueno aff
Madhavi V. Sardeshpande, Barlev Raymond, Vivek V. Ranade

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
FundersDepartment of Science and Technology, Government of Kerala
KeywordsBoilingComputational fluid dynamicsBoiler (water heating)MechanicsFlow (mathematics)Nucleate boilingHeat transferInletTube (container)Work (physics)Critical heat fluxHeat fluxMaterials scienceTwo-phase flowThermodynamicsMass fluxNuclear engineeringMechanical engineeringEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

Boiling flows are encountered in a wide range of industrial applications such as boilers, nuclear reactors, electronic cooling, and various types of chemical reactors. Heat transfer coefficients, flow boiling regimes, flow instabilities, pressure drops, and conditions like dry‐out are some of the key issues in every boiling flow study. It is observed that a limited experimental database is available for the 19 mm ID channel diameter, i.e. a typical tube diameter of steam generator. Therefore, in the present work, the experimental setup was designed for studying boiling flows in a 19 mm ID tube in such a way that different flow regimes occurring in a steam generator tube (from pre‐heating of sub‐cooled water to dry‐out) could be investigated by varying inlet conditions. The reported results cover a reasonable range: 8–27 kW/m 2 and 2.9–5.9 kg/m 2 · s heat and mass flux conditions, respectively. A basic computational flow model was also developed to facilitate interpretation of the obtained data. The presented design of the experimental setup and the approach of mimicking different types of steam generator tubes as well as presented experimental and simulated results are useful for gaining insight into complex boiling flows in tubes, and provide a sound basis for further work in this area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.224
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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